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Amazon Web Services (AWS) on Tuesday announced a new Forward Deployed Engineering (FDE) organization designed to embed engineers within client companies to co-develop agentic AI systems.
The initiative is backed by what Amazon describes as a $1 billion commitment of internal resources, aimed at scaling a model where engineers work alongside customer teams to move AI from experimentation into production systems.
AWS says the goal is to help companies build “agentic-first” systems that are tightly integrated into business processes, rather than standalone tools. A key pitch of the new model is speed. The company notes that its FDE teams are designed to compress deployment timelines from months to days by embedding engineers who can work directly through business, engineering, and security constraints in real time.
These engineers will also use what AWS calls an AI-driven development lifecycle, in which AI systems assist with execution while human engineers oversee and validate the output. Unlike traditional consulting approaches that typically end once a system is delivered, AWS says FDE engagements are designed to leave customers with both working systems and internal capabilities.
Self-sufficiency as a core goal
AWS says customers should not remain dependent on external engineers after deployment ends. According to AWS, “Customers leave AWS FDE deployments with both new solutions and new engineering capabilities.” The company also says organizations will gain “lasting AI skills, workflows, and patterns they can use to innovate independently.”
The model is structured so internal customer teams gradually shift from observing to co-building and eventually operating systems themselves, supported by documentation, knowledge graphs, and trained internal staff.
Competing in a growing FDE race
Amazon is entering a field that has quickly gained momentum across the AI industry. The forward-deployed engineer model was originally popularized by Palantir and has since been adopted in different forms by major AI companies.
According to TechCrunch, tech companies including OpenAI and Anthropic have already launched their own FDE-style ventures, often structured as joint initiatives with external partners. Analysts say the model is becoming popular because many enterprises struggle to turn AI prototypes into production systems without deep technical support embedded inside their organizations.
What makes Amazon’s approach different
Unlike some competitors that have built FDE efforts as external ventures, AWS is keeping the programme fully inside its own organization.
The company says its engineers will be deployed into customer environments alongside purpose-built AI agents, with a focus on long-term reuse of components across industries while still tailoring deployments to each client. AWS also emphasizes governance and security, including customer-controlled environments, encryption, and isolation, so data remains within enterprise boundaries.
AWS says FDE teams are already working with organizations such as the NFL, Southwest Airlines, the NBA, Ricoh, Cox Automotive, and the Allen Institute.
Implications for businesses and tech teams
For enterprises, the promise is faster AI deployment without requiring deep internal expertise up front. Companies get working systems, trained staff, and documentation to maintain them after AWS engineers leave.
But there are trade-offs. The model is labor-intensive, requiring large teams of highly skilled engineers embedded across multiple clients. It also raises questions about long-term dependency during deployment phases and how knowledge transfer actually holds up once external teams exit.
Still, AWS is betting that the upside outweighs the cost: faster production of AI systems and customers who become capable AI builders themselves.
Also read: HP is expanding OpenAI Frontier across global operations, using enterprise AI for engineering, support, sales, and internal workflows.